An Unusual Presentation of a Massive Pulmonary Embolism with Misleading Investigation Results Treated with Tenecteplase
Bibliographic record
Abstract
Background. There is no foolproof strategy to identify a pulmonary embolism (PE) in the emergency department, and atypical presentations are common. Negative test results may mislead physicians away from the diagnosis of PE. Objectives. The current report aims to raise awareness of an unusual presentation of massive PE and its diagnosis and management, in the face of limited evidence in the scientific literature. Case Reports. We report the case of a patient with a negative D-Dimer and a negative Computed Tomography contrast angiography of the chest who was diagnosed twenty-seven hours later with a massive PE, as suggested by a bedside echocardiography. The patient was successfully treated with tenecteplase (TNK). Conclusions/Summary. Pulmonary embolism frequently presents atypically and is often a diagnostic challenge. There is limited literature about the treatment of massive PE. Further research on bedside echocardiography for diagnosing PE in unstable patients is warranted. In addition, further study into new thrombolytic agents like tenecteplase in the context of massive and submassive PE is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".